
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


lowercase    :   Optional[int]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	CLIPConfig

	_A								=	['CLIPEncoderLayer']







	def __init__(   self						:str	,  a						:CLIPConfig		)						->					Optional[Any]:
								super().__init__(a		)

								__UpperCamelCase      :  Any       =   CLIPVisionModelWithProjection(config.vision_config		)

								__UpperCamelCase      :  int       =   nn.Linear(config.vision_config.projection_dim	,  1		)
								__UpperCamelCase      :  Optional[int]       =   nn.Linear(config.vision_config.projection_dim	,  1		)







	@torch.no_grad()
	def   _lowerCamelCase							(   self						:List[str]	,  a						:str	,  a						:int	,  a						:Dict=0.5	,  a						:Dict=0.5		)						->					int:
								__UpperCamelCase      :  int       =   self.vision_model(a		)[0]

								__UpperCamelCase      :  str       =   self.p_head(a		)
								__UpperCamelCase      :  List[str]       =   nsfw_detected.flatten()
								__UpperCamelCase      :  Dict       =   nsfw_detected > p_threshold
								__UpperCamelCase      :  Optional[int]       =   nsfw_detected.tolist()

								if any(a		):
															logger.warning(
															    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
															    " Try again with a different prompt and/or seed."		)

								for idx, nsfw_detected_ in enumerate(a		):
															if nsfw_detected_:
																						__UpperCamelCase      :  Any       =   np.zeros(images[idx].shape		)

								__UpperCamelCase      :  int       =   self.w_head(a		)
								__UpperCamelCase      :  str       =   watermark_detected.flatten()
								__UpperCamelCase      :  List[str]       =   watermark_detected > w_threshold
								__UpperCamelCase      :  Any       =   watermark_detected.tolist()

								if any(a		):
															logger.warning(
															    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
															    " Try again with a different prompt and/or seed."		)

								for idx, watermark_detected_ in enumerate(a		):
															if watermark_detected_:
																						__UpperCamelCase      :  Any       =   np.zeros(images[idx].shape		)

								return images, nsfw_detected, watermark_detected

def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")